This presentation was held at the 2nd Internet of Manufacturing Conference on February 7, 2017, in Munich, Germany. It addresses the need of a new kind of data management to cope with the requirements digital scenarios pose on the industrial enterprise. Motivated by examples, the talk outlines design principles for smart data management and concludes with two leading examples, namely the Industrial Data Space initiative and the Corporate Data League.

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Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017
TURNING DATA INTO VALUE
LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION

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AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples

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Image sources: Audi (2016).
Legend: AGV - Automated Guided Vehicle; VR – Virtual Reality.
Industrial digitization happens in all value creation processes – as the
example of Audi shows
Autonomous AGVs for Modular Production Human Robot Collaboration Autonomous Tugger Trains
Drone Use in Assembly VR in Engineering Predictive Analytics in the Yard

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These developments are a response to fundamental changes manufacturing
companies need to cope with in terms of their production strategy
Production
Volume per
variant
No. of Variants
1850
1913
1955
1980
2000
Ford Model T
VW Beetle
Production
Audi Configurator
Mass
Production
Individualization
»Sharing Economy«
Complexity
Globalization
iPhone
3D Printed Car
Source: Koren (2010), in Bauernhansl (2014); image sources: Wikipedia (2015), Impulse (2015), Audi (2015), O2 (2015), computerbild (2015).

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Image sources: ihs-gmbh.de (2016); silicon.de (2016).
Legend: ERP – Enterprise Resource Planning; LAS – Logistics Assistance System; OEM – Original Equipment Manufacturer.
Data is the key resource in digital value creation networks

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Required is a central information management instance – what Audi refers
to as the »Tower«
Image sources: Audi (2016).

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Image sources: Audi (2016).
The »Tower« is at the core of smart data management
 Conceptual information model of the digital factory
 Source of the »Digital Twins« and »Single Source of the Truth«
 »Data Lake« functionality
 Collection and analysis of manufacturing and supply chain
event data
 Close to real-time process analysis
 Backbone for data analytics and machine learning

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AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples

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Legend: Information flow; Material flow.
Smart data management is the key capability of the digitized industrial
enterprise
Public
Data
Value Chain Data
Commercial
Services
Industrial
Services
Lot-Size 1
End-to-End Customer
Process
Business Ecosystem
Hybrid Offerings
Smart Data
Management
Interoperability
Human-Machine-
Collaboration
Autonomous Systems
Internet of Things
Customer
Production
Networks
Logistics
Networks
Digitized Value PropositionDataDigitized Value Creation

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Industrial data has evolved into a strategic resource with an economic value
Time
Value Contribution
Data as
process
result
Data as
process
enabler
Data as
product
enabler
Data as a
product

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Source: Moody & Walsh (1999).
Despite its intangible nature, industrial data has a value which can be
quantified
Number of users
Share of value
100%
Data
Tangible
Goods
Tangible
Goods
Value
Data
Usage Time
Potential value
Data
Data quality
Value
100%
Data
Integration
Value
Data
Volume
Value
Data

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Source: Otto (2012); Otto (2015).
Many examples exist demonstrating the applicability of valuation
procedures in the data domain
Company Industry Country Data domain
Valuation
approach
Value per record
Retail US
Customer data
including shopping
profile
Market value 1.6 EUR
Social Network US User data Market value 225 USD
Automation and
drives
DE Master data on parts
Production
costs
500 to 5.000 EUR
Agrochemical CH Material master data Use value 184 CHF

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Source: Leveling et al. (2014).
Smart data management is aware of the heterogeneous nature of data
Peripheral data of greater fuzziness, volume,
volatility, heterogeneity…
Peripheral data less controllable, critical,
unambiguous…
Nucleus Data
(Customer data,
product data etc.)
Community Data
(Spatial data, GTIN,
addresses, ISO codes,
EPCIS events etc.)
Open Big Data
(Tweets, social media
streams, sensor data etc.)
Megabytes
Gigabytes
Terabytes
Petabytes

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Smart data management rests on a future-proof data service architecture
Industrial Data Sources
ERP  MES  SCADA  Installed Base etc.
Commercial Data Sources
CRM  Loyalty Programs etc.
Social Data Sources
Facebook  Twitter etc.
Cloud-based Data Storage
Data Source Connectors  Data Space Infrastructure  Shared Information Model
Industrial Data Service Architecture
Data Quality Assurance Mapping/Transformation Integration/Aggregation Data Provenance …
Data Analysis Data Mining Visualization Data Delivery …
Industrial Use-Cases
Preventive Maintenance  Digital
Farming  Supply Chain Visibility
Commercial Use-Cases
Smart Home  Mobility  HealthCare
etc.
Internal Use-Cases
Data as a Process Enabler
Context-free Use
Data as a Product

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Source: VDI (2015).
Smart data management enables digital twins of the real word
Reference Architecture Model Industry 4.0 Administrative Shell Concept
The Administrative Shell stores all data of a hardware or software component in production scenarios
It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way

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A set of design principles guides the transformation to smart data
management
Design Perspective Design Principles Implementation Examples
Strategic principles Productizing of data Data products with clearly defined data elements or
configuration, service levels …
Managing data as an asset Data valuation and pricing, data lifecycle management …
Data co-creating and sharing Collaboration in communities of interest and eco-systems
Organizational principles Governing data in participative ways Transparent responsibilities, digital sovereignty, data owners
in control …
Managing data supply chains and life-cycles end-to-end Data acquisition, pre-processing, processing, distribution, use,
retirement…
Recognizing data quality as probabilistic Dealing with fuzzy and volatile data
with limited traceability
Systems and architecture
principles
Deploying federated architectures Open platforms, linked data…
Decentralizing information security and data sovereignty Data tagging, blockchain technologies…
Sharing data processing resources Cloud platforms, intelligent devices, edge computing

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AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples

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Source: Otto (2016).
The Industrial Data Space addresses the squaring of the data
sovereignty circle
Interoperability
Data Exchange
»Sharing Economy«
Data-centric Services
Data Ownership
Data Privacy and Security
Data Value
Data sovereignty is the capability of a natural person or corporate entity for exclusive self-determination with
regard to its economic data goods

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Data flow
Material flow
Legend: IDS – Industrial Data Space; LSP – Logistics Service Provider; IoT – Internet of Things.
The Industrial Data Space connects various digital platforms and the
internet of things
Public context data
Weather
Factory/Warehouse
LSPElectronic
Marketplace
Traffic
IoT
Cloud
IDS Broker
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
Supply chain planning data
Supply chain event data
Internal process data

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Source: Cf. Kagermann (2015).
The Industrial Data Space defines the data architecture between
smart services and the internet of things

Connected physical platforms Smart Products
Technical infrastructure Smart Spaces
 Industrial Data Space
Service platforms Smart Services
Smart Data Services
(Alerting, Monitoring, Data quality etc.)
Basic Data Services
(Fusion, Mapping, Aggregation etc.)
 Use restrictions attached to the data
 Secure data supply chain
 Data Fusion
 Certified software endpoints
 Multiple use scenarios
 Federated governance models

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NB: As per December 2016.
The initiative rests on solid and continuously growing industry
commitment organized in the Industrial Data Space Association

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Image source: Competence Center Corporate Data Quality (2016).
The »CDQ Framework« is a standard capability model for
managing the data core

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Source: CDQ AG; Corporate Data League (2016).
The Corporate Data League is a community approach for managing
business partner data

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Prof. Dr.-Ing. Boris Otto
Fraunhofer ISST
Managing Director
Boris.Otto@isst.fraunhofer.de
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Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017
TURNING DATA INTO VALUE
LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION